Grammar induction.
Pioneering work on the learning of grammars from examples often attempted to construct probabilistic context-free grammars that generated the target language \(\mathcal {L}\)  [1]}.
Subsequent attempts employed a wider variety of tactics including Bayesian methods [2]} and genetic programming [3]}.
The learning was done chiefly from positive examples, though methods for automatic negative example generation from positive examples were later introduced [4]}.
Similarly to above, contemporary work on grammar induction is almost exclusively guided by the desired applications in NLP rather than interest in theory of computation.
As such, black-box recurrent neural automata [5]}, reinforcement learning models [6]}, and partially interpretable structured attention [7]} and transformer [8]} models dominate the sub-field at present.
